scholarly journals Statistically-Based Trend Analysis of MTInSAR Displacement Time Series

2021 ◽  
Vol 13 (12) ◽  
pp. 2302
Author(s):  
Fabio Bovenga ◽  
Guido Pasquariello ◽  
Alberto Refice
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
High Rate ◽  
Automatic Identification ◽  
Warning Signals ◽  
Temporal Sampling ◽  
Long Time ◽  
Multi Temporal ◽  
Minimum Number ◽  
Linear Trends

Current multi-temporal interferometric Synthetic Aperture Radar (MTInSAR) datasets cover long time periods with regular temporal sampling. This allows high-rate and non-linear trends to be observed, which typically characterize pre-failure warning signals. In order to fully exploit the content of MTInSAR products, methods are needed for the automatic identification of relevant changes along displacement time series and the classification of the targets on the ground according to their kinematic regime. This work reviews some of the classical procedures for model ranking, based on statistical indices, which are applied to the characterization of MTInSAR displacement time series, and introduces a new quality index based on the Fisher distribution. Then, we propose a procedure to recognize automatically the minimum number of parameters needed to model a given time series reliably within a predefined confidence level. The method, though general, is explored here for polynomial models, which can be used in particular to approximate satisfactorily and with computational efficiency the piecewise linear trends that are generally used to model warning signals preceding the failure of natural and artificial structures. The algorithm performance is evaluated under simulated scenarios. Finally, the proposed procedure is also demonstrated on displacement time series derived by the processing of Sentinel-1 data.

scholarly journals Change sign detection with differential MDL change statistics and its applications to COVID-19 pandemic analysis

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kenji Yamanishi ◽  
Linchuan Xu ◽  
Ryo Yuki ◽  
Shintaro Fukushima ◽  
Chuan-hao Lin
Keyword(s):  
Time Series ◽  
Early Warning ◽  
Data Science ◽  
Minimum Description Length ◽  
Length Change ◽  
Warning Signals ◽  
Early Warning Signals ◽  
New Information ◽  
Long Time ◽  
Synthetic Datasets

AbstractWe are concerned with the issue of detecting changes and their signs from a data stream. For example, when given time series of COVID-19 cases in a region, we may raise early warning signals of an epidemic by detecting signs of changes in the data. We propose a novel methodology to address this issue. The key idea is to employ a new information-theoretic notion, which we call the differential minimum description length change statistics (D-MDL), for measuring the scores of change sign. We first give a fundamental theory for D-MDL. We then demonstrate its effectiveness using synthetic datasets. We apply it to detecting early warning signals of the COVID-19 epidemic using time series of the cases for individual countries. We empirically demonstrate that D-MDL is able to raise early warning signals of events such as significant increase/decrease of cases. Remarkably, for about $$64\%$$ 64 % of the events of significant increase of cases in studied countries, our method can detect warning signals as early as nearly six days on average before the events, buying considerably long time for making responses. We further relate the warning signals to the dynamics of the basic reproduction number R0 and the timing of social distancing. The results show that our method is a promising approach to the epidemic analysis from a data science viewpoint.

Bayesian detection of piecewise linear trends in replicated time-series with application to growth data modelling v1 (protocols.io.4figtke)

protocols.io ◽  
2019 ◽  
Author(s):  
Norman van ◽  
Panagiotis Papastamoulis ◽  
Takanori Furukawa ◽  
Magnus Rattray ◽  
Mike Bromley ◽  
...  
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
Data Modelling ◽  
Growth Data ◽  
Linear Trends

Tectonic Deformation and Surface Processes of the Tibetan Plateau Constrained by Time Series Analysis of Sentinel-1 InSAR Data

2020 ◽  
Author(s):  
Robert Zinke ◽  
Gilles Peltzer ◽  
Eric Fielding ◽  
Simran Sangha ◽  
David Bekaert ◽  
...  
Keyword(s):  
Time Series ◽  
Tibetan Plateau ◽  
Surface Deformation ◽  
Active Faults ◽  
High Rate ◽  
Surface Processes ◽  
Tectonic Deformation ◽  
The Tibetan Plateau ◽  
Data Set ◽  
Temporal Sampling

<p>We quantify deformation patterns resulting from tectonic motions and surface processes across the central Tibetan Plateau (29–45ºN, 83–92ºE) since late 2014 using ascending and descending passes of the Sentinel-1A and -1B radar satellites. The broad spatial extent of these data (> 10<sup>6</sup> km<sup>2</sup>), fine spatial resolution (originally 90 m pixels, resampled to 270 m pixels), and high rate of temporal sampling (12–24-day orbit repeat time) offer unprecedented resolution of surface deformation in space and time. To process such an extensive data set – including more than 100 dates and 300 interferograms per track thus far – we leverage the Advanced Rapid Imaging and Analysis (ARIA) standardized interferometric synthetic aperture radar (InSAR) products and toolbox. We construct time series of surface deformation constrained from our Sentinel-1 interferograms using the small baseline subset approach implemented by the Miami InSAR time series software in Python (MintPy). Our preliminary results from three Sentinel-1 orbits (two descending and one ascending; each comprising 10 frames along track) allow us to quantify deformation in the satellite lines of sight. Combinations of ascending and descending track measurements are used to approximate east-west and vertical ground velocities. The resulting velocity fields will provide a more complete and accurate picture of interseismic strain accumulation rates across active faults in the region such as the Altyn Tagh and Kunlun faults, and allow us to study surface processes such as permafrost active layer dynamics and isostatic adjustment due to lake level changes in unparalleled scope and detail.</p>

scholarly journals DEFORMATION MONITORING OF HIGH-LATITUDE PERMAFROST REGION OF NORTHEASTERN CHINA WITH TIME SERIES INSAR TECHNIQUE

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1777-1780 ◽  
Author(s):  
T. Qu ◽  
Q. Xu ◽  
W. Shan ◽  
Z. Li ◽  
M. Shan ◽  
...  
Keyword(s):  
Time Series ◽  
High Latitude ◽  
Northeast China ◽  
Deformation Monitoring ◽  
Permafrost Region ◽  
Local Climate ◽  
Spatial Distribution Characteristics ◽  
Long Time ◽  
Multi Temporal ◽  
Local Climate Change

<p><strong>Abstract.</strong> Permafrost distributed in northeast China is the only high-altitude permafrost in China. The deformation monitoring over this permafrost region is of great importance to local climate change and ecological environments. This study focuses on the deformation monitoring of high-latitude permafrost in northeast China with time series InSAR technique. The spatial distribution characteristics, the annual deformation rates and the temporal deformation evolutions of permafrost could be retrieved from multi-temporal InSAR processing with Sentinel-1 TOPS datasets. This work concludes that time series InSAR technique could help to retrieve a comprehensive and reliable permafrost deformation, while a long time-series of displacements facilitated to better understand permafrost kinematics.</p>

scholarly journals Bayesian detection of piecewise linear trends in replicated time-series with application to growth data modelling v2 (protocols.io.5x8g7rw)

protocols.io ◽  
2019 ◽  
Author(s):  
Norman van ◽  
Panagiotis Papastamoulis ◽  
Takanori Furukawa ◽  
Magnus Rattray ◽  
Mike Bromley ◽  
...  
Keyword(s):  
Time Series ◽  
Piecewise Linear ◽  
Data Modelling ◽  
Growth Data ◽  
Linear Trends

Automatic Identification of Autoregressive Integrated Moving Average Time Series

1982 ◽  
Vol 14 (3) ◽  
pp. 156-166 ◽  
Author(s):  
Chin-Sheng Alan Kang ◽  
David D. Bedworth ◽  
Dwayne A. Rollier
Keyword(s):  
Time Series ◽  
Moving Average ◽  
Automatic Identification ◽  
Autoregressive Integrated Moving Average

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

New determinations of tides on the north-western Ross Ice Shelf

2020 ◽  
pp. 1-14
Author(s):  
Richard D. Ray ◽  
Kristine M. Larson ◽  
Bruce J. Haines
Keyword(s):  
Time Series ◽  
Tide Gauge ◽  
High Rate ◽  
Ice Shelf ◽  
Ross Ice Shelf ◽  
The North ◽  
Global Positioning ◽  
North Western

Abstract New determinations of ocean tides are extracted from high-rate Global Positioning System (GPS) solutions at nine stations sitting on the Ross Ice Shelf. Five are multi-year time series. Three older time series are only 2–3 weeks long. These are not ideal, but they are still useful because they provide the only in situ tide observations in that sector of the ice shelf. The long tide-gauge observations from Scott Base and Cape Roberts are also reanalysed. They allow determination of some previously neglected tidal phenomena in this region, such as third-degree tides, and they provide context for analysis of the shorter datasets. The semidiurnal tides are small at all sites, yet M2 undergoes a clear seasonal cycle, which was first noted by Sir George Darwin while studying measurements from the Discovery expedition. Darwin saw a much larger modulation than we observe, and we consider possible explanations - instrumental or climatic - for this difference.

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